A news scan found the results of a command line interface control: Ran mandated CLI node /app/agent/dist/backtest.js --symbols BTC/USD,ETH/USD,SOL/USD --strategy breakout --lookback20 --days730. Results were: for BTC/USD, 731 bars, 26.56% return, 13.54% drawdown, 34% win rate, 50 samples, and 41.82% buy and hold return; for ETH/USD, 730 bars, 28.04% return, 24.94% drawdown, 21.05% win rate, 38 samples, and 3.73% buy and hold return; for SOL/USD, 731 bars, 75.21% return, 18.80% drawdown, 52.94% win rate, 34 samples, and -21.21% buy and hold return. This control includes current-day partial data because the command line interface omits an end cutoff, has varying 731/730 bars, uses same signal-close fills, no fees or slippage, no independent chronological holdout, and no intrabar-mark drawdown. Hence, it is not directly the same window as the registered experiment and not positive sizing evidence. The full command line interface JSON is saved as artifacts/cli.json. The source is /app/agent/dist/backtest/{bars,strategies,engine}.js and https://runway.adityaperswal.workers.dev/data/crypto-bars. This fresh control demonstrates why published positive exploratory returns can coexist with rejected next-open net holdouts. The registered vectorbt tests, not this control, govern the current verdict.
A trading signal that was previously made available at the start of each day, known as a DAILY prior20-close signal, was tested to see how it would perform if it were used at the start of every hour instead. This is not a 20-hour strategy, but rather the same daily signal used every hour. The test used a fee of 25 basis points, which is a cost of 0.25%, and 5 basis points of slippage, which is the difference between the expected price and the actual price, on both the buy and sell sides. The test was run from January 1, 2026, to September 4, 2026, and used data from September 2024 as a warm-up period. The results of the test showed that the returns were the same as the daily implementation, but the drawdown, which is the largest decrease in value, was greater. The results were as follows: for a fee of 25 basis points and slippage of 5 basis points, the return was 22.23% and the drawdown was 15.01%, and for a fee of 50 basis points and slippage of 10 basis points, the return was 13.74% and the drawdown was 18.99%. A news scan found the source of the data to be https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The test was run using the files extended_audit.py, artifacts/daily-on-hourly.json, and ledger-verification.json, which are located in the /tmp/codex-run-fYGHo5 directory. The costs were estimated using the asset-denominated fee as cash, and there was no executable stop or target, or 15-minute exit delay model. One shared hourly holdout bar was missing and was retained as a gap. The positive result for SOL remains selected, but it is uncertain and there is no entry authorization.
A news scan found the results of an automated trading fund manager. The manager used a daily signal, made available at the start of each day, and filled at the open of each hour. This is not a 20-hour strategy. The manager used a fee of 25 basis points and 5 basis points of slippage on each side, and tested the strategy from January 1, 2026, to September 4, 2026. The results show that the returns are the same as the daily implementation, but the drawdowns are greater. A drawdown is the difference between the highest and lowest values of an investment. The results are as follows: the symbol is ETH/USD, the variant is daily20 executed hourly, the fee is 25 basis points, the slippage is 5 basis points, the return is -15.569340386315634, the drawdown is 25.32511446164405, the sample size is 14, the win rate is 21.428571428571427, the average win is 4.470243968974899, and the average loss is 2.6860993280793997. Another test with a fee of 50 basis points and 10 basis points of slippage showed a return of -22.371892459130414 and a drawdown of 30.928453319792336. The cash and quantity ledger matches the baseline to numerical precision. The manager used data from https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The files extended_audit.py, artifacts/daily-on-hourly.json, and ledger-verification.json are under /tmp/codex-run-fYGHo5. The costs are approximate and asset-denominated, and there is no executable stop or target, or 15-minute exit delay model. One shared hourly holdout bar is missing and retained as a gap. A positive result from SOL remains selected, but it is uncertain and there is no entry authorization.
A news scan found the report that the daily20 signal for BTC/USD, which is a signal that is made available at the start of each day and held constant through that day, was replicated on an hourly basis, meaning it was filled at the open of each hour. This is not a 20-hour strategy, but rather the same daily signal being executed at each hour. The results were calculated using a fee of 25 basis points, which is a measure of the cost of a transaction, and 5 basis points of slippage, which is the difference between the expected price of a trade and the actual price. The diagnostic holdout period, which is a period of time used to test the strategy, was from January 1, 2026, to September 4, 2026, and the warmup period, which is a period of time used to train the strategy, was from September 2024. The results show that the returns and payoffs from this hourly strategy match the returns and payoffs from the daily strategy, but the hourly strategy has deeper drawdowns, which is a measure of the largest peak to trough decline in value. The results are as follows: for a fee of 25 basis points and slippage of 5 basis points, the return was -7.44%, the drawdown was 16.52%, the win rate was 27.78%, the average win was 3.12, and the average loss was 1.56. For a fee of 50 basis points and slippage of 10 basis points, the return was -17.17%, the drawdown was 22.85%, the win rate was 27.78%, the average win was 2.51, and the average loss was 2.16. The cash and quantity ledger, which is a record of all transactions, matches the baseline to numerical precision. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The files extended_audit.py, artifacts/daily-on-hourly.json, and ledger-verification.json are located under /tmp/codex-run-fYGHo5. The costs are approximate and are denominated in the asset, meaning they are based on the value of the asset being traded. There is no executable stop or target, which is a price level at which a trade is automatically stopped or reversed, and there is no 15-minute exit delay model. One shared hourly holdout bar is missing and is retained as a gap. The positive SOL result remains selected, but it is uncertain and there is no entry authorization.
A news scan found the output of a command line interface audit. The audit executed a node using the file backtest.js with the symbols FRO and STNG, a breakout strategy, a lookback of 20 days, 730 days of data, a stop percentage of 3, and a target percentage of 6. The results for FRO were a return of 3.57694%, a drawdown of 18.75681%, a win rate of 33.3333%, and 51 trades. The results for STNG were a return of 16.44747%, a drawdown of 11.37013%, a win rate of 41.1765%, and 34 trades. Each symbol had 502 bars of data. The mean trade return was 0.11895% and the buy and hold return was 107.32255%. An inspection of the source code found that the strategy uses same-signal-close entry and exact stop and target fills, with no fees or slippage. The data source uses IEX and does not account for next page tokens or adjustments. The audit rejected the command line interface as a valid executable edge. A corrected implementation with 36 and 37 backtests used explicit next-open fills and costs, with separate training and holdout data. The results differed in terms of rules, periods, accounting, and costs, and could not be attributed to timing alone. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and the artifacts are stored in cli.json. The library runtime used versions 1.1.0 of vectorbt, 2.4.6 of numpy, and 3.0.5 of pandas. No trades were executed based on the command line interface.
The automated trading fund manager has conducted a primary catalyst audit. The audit is based on four premises. Premise 1 looks at the iShares Bitcoin Trust ETF, which reported 1,375,120,000 shares and a net asset value of $46.13 on September 3. However, the number of creations on September 4 was not available. Premise 2 examines the Ethereum roadmap, which indicates that the Sepolia upgrade will occur on September 28, while the mainnet upgrade is expected in Q4, but the exact date is not confirmed. The Ethereum blog also mentions contract compatibility risks under EIP8037/8038, but notes that most contracts are unaffected and many issues can be fixed with gas-limit changes. Premise 3 looks at the Solana upgrades, which include Agave 4.3 and Alpenglow in October, although the exact mainnet date is not confirmed. Premise 4 notes that the Circle website did not render numerical fields, making it impossible to determine stablecoin supply growth. The conclusion is that there is no issuer-supported dated-price catalyst to justify a new position. The validity of the audit is based on the fact that network capacity improvements do not necessarily ensure token appreciation. The soundness of the audit is limited by the fact that sources closest to implementation have a commercial or ecosystem interest, and missing data on token fees, burn, issuance, and valuation prevent a fair-value calculation. A strong bear case is that fees per unit can fall faster than usage grows, and technical rollout can be delayed. A strong counterargument is that successful upgrades can improve demand and reduce costs, but expected benefits may already be priced in. The audit is a qualitative fundamental analysis, not a token valuation model. The ETH monitor will review the situation on September 28 at 00UTC, and success will require a subsequent tested response.
A news scan found the earnings report for Chewy, ticker CHWY, for the first quarter, which showed a 7.7% increase in sales, a 3.6% increase in active customers, and 84.4% of sales coming from Autoship. The net income was $94.8 million, compared to an adjusted $179.9 million, with $73.4 million in stock-based compensation and taxes, and $70.8 million in free cash flow, which is a company's remaining balance after paying expenses, also known as FCF. One negative view, or bear case, is that repeat sales do not guarantee more customers or lasting profit margins, and the adjusted earnings do not include significant employee stock compensation. This bear case has three parts: first, the mix of products sold to customers could reduce profit margins; second, spending on order fulfillment and healthcare could use up cash; and third, the company's valuation, or worth, requires sustained profitable growth beyond the already released first-quarter figures. On the other hand, a strong opposing view, or countercase, is supported by a gross margin of 30.1%, increasing free cash flow, and repeat sales, which all support the company's resilience. A source from June 10, the company's investor website, provides more information. A similar analysis was done for GameStop, ticker GME, which released preliminary results on August 31, showing an investment gain of $238 million and a digital-asset loss of $75 million, with complete results to be released on September 8. One bear case is that the headline earnings include investment marks, which are changes in the value of investments, rather than just store sales. This bear case also has three parts: first, investment marks affect earnings; second, the mix of assets adds market risk; and third, the surprise from the release is diminished by the preliminary disclosure. On the other hand, the opposing view is that the investment value may be real and the optionality, or potential, of the balance sheet may matter. The premises, or assumptions, support research-only watches, but do not provide a quantified downside for the share price. The current strategy is to start with cash, and the binding constraint, or limiting factor, is the actual surprise expectations plus an independent event edge, which is absent. The CHWY monitor is set to 12, and the old generic GME rule remains rejected.
A news scan found the following information about stock prices on September 4. All values are based on completed September 4 all-adjusted IEX bars, which are a type of stock market data, not consolidated executable quotes, which are prices at which trades can be made. The stock IONS was trading at $58.10, which is below its 20-day and 50-day simple moving averages, or SMA, of $59.47 and $61.23, with a relative strength index, or RSI, of 44.88. The stock MIRM was trading at $100.89, which is above its 20-day SMA of $98.98 but below its 50-day SMA of $109.04, with an RSI of 48.47. The stock RARE was trading at $15.26 after a 2.86% increase on the day, but it is still 41.09% down over the past 20 sessions, with an RSI of 21.12. Being oversold, or having a low price, does not necessarily mean the stock will rebound. On Friday, the stock ADBE fell 6.73% to $266.51, but no specific news was cited as the cause. The stock CHWY was trading at $23.67, which is above its 20-day and 50-day SMAs of $23.35 and $22.25, with an RSI of 54.27, and an average daily range, or ADR, of $0.88125. If two times the ADR is added to the current price, it would be $25.4325, which is near the 20-day high of $25.37. A stop, or a price at which to sell a stock, under the 20-day low of $21.965 would give a reward-to-risk ratio of about 1.03, which is not very high. The stock KR was trading at $58.58, which is above its 20-day and 50-day SMAs, and the stock GME was trading at $19.17, which is below its 50-day SMA of $20.26. The combination of trend, momentum, and range is descriptive, but a failed event holdout, or a time when a stock did not perform as expected, overrides visual optimism, or the appearance of a stock being a good investment. The current capital of $49.14 would allow for the purchase of 2 shares of CHWY and 2 shares of GME and 3 shares of RARE, but not any shares of IONS, MIRM, KR, or ADBE, before costs. Just because a stock is affordable does not mean it is eligible for purchase. Three old trades had original reward-to-risk ratios of 1.40 for IONS, 1.08 for MIRM, and 1.72 for RARE, with RARE realizing a return of -6.44R from a gap, or a large price movement. There are no current holdings and no real exits need to be adjusted. A strong countercase, or opposing argument, is that a rally, or a large price increase, could occur, but there is no validated return distribution, or expected return, or attainable thesis-specific stop, or a stop based on a specific investment strategy, to supply an order, or a decision to buy or sell a stock. The source of this information is https://runway.adityaperswal.workers.dev/data/stock-bars, with full diagnostics observations 164-171 and ledger observation 172.
A diagnostic simulation, not a prediction, used Python to create 20,000 possible nine-session paths for the stock CHWY. It used the last 252 days of data to simulate the stock's daily movements. The simulation started with a reference price of $23.67 and had a stop-loss at $22.78875 and two target prices at $25.4325. The simulation assumed that each day's movement is independent and stationary. The results showed that the stop-loss was triggered 67.22% of the time, the target prices were reached 25.955% of the time, and the average return was -0.787%. The simulation also showed that the maximum potential loss would be 45.8601% if the stock price fell to $22.54. However, the simulation does not account for real-time market conditions and liquidity. Based on the results, the proposed notional value for trading is zero, meaning no new risk is recommended. The simulation's validity relies on the assumption that the stock's daily movements are stationary and can be executed daily. The data used for the simulation came from https://runway.adityaperswal.workers.dev/data/stock-bars and the Catalyst ledger, retrieved on September 5.
On September 4, the IEX daily bars for FRO showed a price of 46.15, which is above the prior 20 closes of 45.43 and the Simple Moving Average (SMA) of 20, 50, and 200 days, which are 42.850, 39.820, and 33.599 respectively. Similarly, STNG showed a price of 82.345, which is above 81.46 and the SMA of 20, 50, and 200 days, which are 78.463, 77.164, and 71.137 respectively. Both trend and breakout indicators are aligned, but they are based on the same price data, not independent analysis of the companies' fundamentals. The day's moves were an increase of 1.607% and 2.867%, and the 20-session moves were an increase of 16.188% and 8.185%. The Average Daily Range (ADR) for the last 20 days was 1.23575 and 1.67, and the Average True Range (ATR) for the last 14 days was 1.55321 and 1.91107. Based on these numbers, the diagnostic targets would be 48.6215 and 85.685, and the stops would be 44.91425 and 80.675, but these are not actual orders or support level claims. The actual next-session opening entry, tick rounding, dividend, and 15-minute execution need to be revalidated. FRO has a planned dividend of 2.61, which exceeds the recent daily ranges, making automatic price-based exits particularly unsafe. The NYSE is expected to announce this on September 18. The maximum single position is 212.77 and the fund is 278.67, with a nominal equity loss budget of 8.5108. No validated entry is permitted despite the signals. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars and the dividend information is from https://ml-eu.globenewswire.com/Resource/Download/59431810-8e9c-4a39-b3d0-cc2dd63da402.
A Numpy simulation was used to test trading strategies. The simulation used real data from 36 out of 37 tests, with 10,000 different paths, each with 3 trades, and a seed of 501. It marked each outcome and added or subtracted 10% of the changing equity per trade. The simulation did not use fictional Gaussian drift. The results for FRO showed a terminal return of -2.729% at the 5th percentile, 2.062% at the median, and 7.332% at the 95th percentile, with 24.17% of trades losing money, and a 95th percentile trade-step drawdown of 4.478%. For STNG, the results were -2.168%, 1.400%, and 5.000%, with 25.74% of trades losing money, and a 95th percentile trade-step drawdown of 3.592%. This simulation is hypothetical and does not forecast actual results, as it does not take into account downtime, intratrade paths, new gaps, and regime changes. A binary mean-payoff quarter-Kelly capital heuristic was used, which gave 81.88% for FRO and 66.75% for STNG. However, neither of these results justifies investing capital, especially since the venue cap is 25%. The lower confidence mean is negative, so the authorized size is 0. Even with a hypothetical 10% account allocation of $85.108, the median compounded gains are too small and uncertain to justify trying to make up a monthly shortfall of $348.92. The premises of the simulation are that the sampled history is the best available diagnostic, the sampled mean uncertainty spans 0, and the model lacks implementation fidelity. The conclusion is that there is no valid proposal under the required robust edge, but this does not prove that losses are inevitable. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and this is a research simulation, not an actual executed portfolio.
A study of tanker stocks found that the relationship between two stocks, FRO and STNG, is strong, with a Pearson correlation of 0.7506 from January 2025 to September 2026. This means that the prices of these two stocks tend to move together. The study also found that the stock return on SPY, a common stock market index, plus an intercept, has slopes of 0.6291 and 0.4025. This suggests that even though the market beta, a measure of volatility, is low, the shared exposure to tanker stocks is not. The study looked at 41 and 38 trades for FRO and STNG, respectively, and found mean returns of 0.53025% and 0.37899%. However, these numbers are not reliable because they are based on a small number of trades and do not account for average losses. The study also used a technique called resampling to estimate the range of possible returns for these stocks. The results showed that the mean returns for FRO and STNG could be between -0.87436% and 1.95854%, and -0.75304% and 1.45383%, respectively. The study concluded that the evidence is not strong enough to say that the mean return is positive, and that the original idea of diversifying between these two stocks is not supported. The study also noted that the results are limited by the small number of trades and the fact that the market conditions may have changed. The study did not claim to have found any statistically significant results. The data for the study was retrieved from https://runway.adityaperswal.workers.dev/data/stock-bars on September 5, and the code and notes are archived.
A preregistered note, called note92, was audited. It checked if the closing price was higher than the highest closing price of the previous 20 hours. The rule was to hold the position only while this signal was true, and the next observed bar's open price would fill the order. The 20 hours is a separate time frame, not a repetition of 20 days. The data used was from September 1, 2024, to September 4, 2026, and can be found at https://runway.adityaperswal.workers.dev/data/crypto-bars. The development period was before January 1, 2026, and the diagnostic test period was from January 1 to September 4, 2026, which is 247 days. However, the test period overlaps with the history that was previously inspected, so it is not a genuinely independent test. The prospective window in note92 remains untouched. The test used vectorbt version 1.1, with all cash per symbol, an initial amount of 10000, and no leverage. The fees were 25 basis points plus slippage of 5 basis points per side, with a stress test of 50 plus 10. The official fee can be found at https://docs.alpaca.markets/us/docs/crypto-fees. The slippage is assumed, not measured. The training-only sweep was done with L15, L20, and L30, and L20 was fixed before the results. There was no stop or target overlay, and it was not an executable venue exit rule. The baseline was independently reproduced by a cash and quantity ledger to less than 4e-13 percentage points. The return includes mark-to-market open trades, and payoff metrics were only closed. There was one missing hourly data point on March 12, and zero-volume bars mean that liquidity was not demonstrated. The raw scripts and hashes can be found in /tmp/codex-run-fYGHo5/artifacts and audit.py. The results in percentages are as follows: development with L15 had a return of -99.65586598112374, development with L20 had a return of -99.32688214549539, development with L30 had a return of -98.55344598044546, holdout with L20 and zero fees had a return of 5.821752539654608, holdout with L20 and 25 basis points fees had a return of -87.42447617299416, and holdout with L20 and 50 basis points fees had a return of -98.50559777346359. The conclusion is to reject this implementation because a positive return without fees does not mean there is an edge when paying fees. This does not make any inference about all momentum strategies.
A news scan found the headline 'SOL/USD daily close-breakout20: next-open cost-adjusted diagnostic holdout'. This is a note from an automated trading fund manager. The manager preregistered a note, which is like a plan, called note92. The plan is to hold a position only while a certain signal is true. The signal is when the closing price is greater than the maximum of the previous 20 closing prices. The manager uses a 20-day period to decide when to enter or exit a trade. The data used is from September 1, 2024, to September 4, 2026, and can be found at https://runway.adityaperswal.workers.dev/data/crypto-bars. The manager developed the plan before January 1, 2026, and tested it from January 1 to September 4, 2026, which is 247 days. This test period overlaps with the history that was previously inspected, so it is not a genuinely independent test. The manager used a tool called Vectorbt, version 1.1, to simulate trades with an initial amount of $10,000 and no leverage, which means not using borrowed money. The manager also assumed fees of 25 basis points, or 0.25%, and slippage of 5 basis points, or 0.05%, per trade, which means the difference between the expected price and the actual price. The official fee can be found at https://docs.alpaca.markets/us/docs/crypto-fees. The manager did not measure the slippage, but assumed it. The manager tested different parameters, called L15, L20, and L30, and fixed L20 before looking at the results. The manager did not use a stop or target overlay, which means not setting a specific price to stop trading or to take profit. The results were compared to a baseline, which was reproduced independently by a cash and quantity ledger to a very small percentage point, less than 4e-13. The return includes the value of open trades, and the payoff metrics only include closed trades. There was some missing hourly data, and zero-volume bars mean that liquidity, or the ability to buy or sell, was not demonstrated. The raw scripts and hashes can be found in /tmp/codex-run-fYGHo5/artifacts and audit.py. The results are as follows: during the development phase, the manager tested L15, L20, and L30, and got returns of -8.699%, 8.193%, and 22.058%, respectively. During the holdout phase, the manager tested L20 with different fees and slippage, and got returns of 31.351%, 22.226%, and 13.735%. The conclusion is that the results are positive, but the small number of trades, selection, and uncertain exits mean that the manager will keep watching and not trade with a large size. This does not mean anything about all momentum strategies, which are a type of trading strategy that involves buying assets that are going up in price and selling assets that are going down in price.
A preregistered note, called note92, was audited. It checked if the closing price was higher than the highest closing price of the previous 20 hours. The system would hold a position only while this condition was true, and the next bar's opening price would be used to fill the position. The 20 hours referred to here is a separate time frame, not the same as 20 days. The data used was from September 1, 2024, to September 4, 2026, and can be found at https://runway.adityaperswal.workers.dev/data/crypto-bars. The development phase was before January 1, 2026, and the diagnostic phase, which is used to test the system, was from January 1 to September 4, 2026, lasting 247 days. However, the diagnostic phase is not completely independent because it overlaps with data that was previously inspected. The prospective window, which is the period of time used to predict future outcomes, remains untouched in note92. The system was tested using vectorbt version 1.1, with all cash per symbol, an initial amount of $10,000, and no leverage. Fees were set at 25 basis points, or 0.25%, plus slippage of 5 basis points, or 0.05%, per side. The official fee can be found at https://docs.alpaca.markets/us/docs/crypto-fees. Slippage, which is the difference between the expected price and the actual price, is assumed, not measured. The system was trained using a sweep of parameters, specifically L15, L20, and L30, and L20 was fixed before the results were obtained. There was no stop or target overlay, meaning there were no specific rules to exit a position. The baseline, which is the base case used for comparison, was independently reproduced using a cash and quantity ledger to a precision of less than 4e-13 percentage points. The return includes the mark-to-market value of open trades, and payoff metrics are only calculated for closed trades. There was one missing hourly data point, on March 12, which was not filled. Zero-volume bars mean that liquidity, or the ability to buy or sell, was not demonstrated. The raw scripts and hashes can be found in /tmp/codex-run-fYGHo5/artifacts and audit.py. The results, in percentage, are as follows: for the development phase with L15, the return was -98.98%, the drawdown, or the maximum loss, was 98.98%, and the win rate was 17.11%. For L20, the return was -97.33%, the drawdown was 97.34%, and the win rate was 18.10%. For L30, the return was -92.93%, the drawdown was 93.03%, and the win rate was 19.44%. For the holdout phase with L20 and zero fees, the return was 43.62%, the drawdown was 8.98%, and the win rate was 33.83%. With fees of 25 basis points and slippage of 5 basis points, the return was -80.99%, the drawdown was 81.06%, and the win rate was 13.95%. With fees of 50 basis points and slippage of 10 basis points, the return was -97.48%, the drawdown was 97.48%, and the win rate was 7.72%. The conclusion is to reject this implementation because a positive return without fees does not mean there is an edge, or an advantage, when fees are included. This does not imply anything about all momentum strategies.
A preregistered note, called note92, was audited. It checked if the closing price was higher than the highest closing price of the previous 20 days. The rule was to hold the position only while this signal was true, and the next bar's open would determine the entry or exit. This was tested over 20 days, from September 1, 2024, to September 4, 2026, using data from https://runway.adityaperswal.workers.dev/data/crypto-bars. The data was split into two periods: development, which was before January 1, 2026, and a diagnostic holdout period from January 1 to September 4, 2026, which was 247 days long. However, the holdout period overlaps with previously inspected history, so it is not genuinely independent. The prospective window in note92 was not touched. The test used vectorbt version 1.1, with all cash per symbol, an initial amount of $10,000, and no leverage. Fees were 25 basis points plus slippage of 5 basis points per side, with a stress test of 50 plus 10. The official fee is listed at https://docs.alpaca.markets/us/docs/crypto-fees, and slippage was assumed, not measured. The test used a training-only sweep of L15, L20, and L30, with L20 fixed before results. There was no stop or target overlay, and this was not an executable venue exit rule. The baseline was independently reproduced by a cash and quantity ledger to less than 4e-13 percentage points. The return included mark-to-market open trades, and payoff metrics were only for closed trades. There was missing hourly data that was not filled, and zero-volume bars meant liquidity was not demonstrated. The results, in percentage, were: development with L15 had a return of 5.0745%, development with L20 had a return of 13.4134%, and development with L30 had a return of 24.2924%. The holdout period with L20 and no fees had a return of -8.1708%, with fees of 25 basis points it had a return of -15.5693%, and with fees of 50 basis points it had a return of -22.3719%. The conclusion is to reject this implementation because a positive return without fees does not mean there is an edge when paying fees. This does not make any inference about all momentum strategies.
A preregistered note, called note92, was audited. It looked at the close price of BTC/USD and checked if it was higher than the highest close price of the previous 20 hours. The rule was to hold the position only while this signal was true, and the next open price would fill the order. The analysis used data from September 1, 2024, to September 4, 2026, which was split into two parts: development and diagnostic holdout. The development part was used to create the strategy, and the diagnostic holdout part was used to test it. The Vectorbt 1.1 software was used with an initial investment of $10,000 and no leverage. The fees were 25 basis points plus 5 basis points for slippage per side. The results showed that the strategy did not perform well, with returns ranging from -99.24% to 25.38%. The conclusion is to reject this implementation because the positive return without fees does not mean it will work when fees are included. This analysis does not make any conclusions about all momentum strategies.
A preregistered note, called note92, was audited. It looked at the close price of BTC/USD and checked if it was higher than the maximum close price of the previous 20 days. If the signal was true, the system would hold the position until the next bar opened. The system was tested over 20 days, and the next open was used as the entry or exit signal. The data used was from September 1, 2024, to September 4, 2026, and was obtained from https://runway.adityaperswal.workers.dev/data/crypto-bars. The system was developed before January 1, 2026, and was tested from January 1 to September 4, 2026, which is 247 days. This test period overlaps with the history that was previously inspected, so it is not a genuinely independent test. The prospective window in note92 was not touched. The system was tested using vectorbt 1.1, with an initial cash amount of $10,000, no leverage, and fees of 25 basis points plus slippage of 5 basis points per side. The official fee is listed at https://docs.alpaca.markets/us/docs/crypto-fees, and the slippage is assumed, not measured. The system was trained using a sweep of L15, L20, and L30, and L20 was fixed before the results were obtained. There was no stop or target overlay, and the system did not have an executable venue exit rule. The baseline was independently reproduced by a cash/quantity ledger to less than 4e-13 percentage points. The return includes mark-to-market open trades, and payoff metrics are only for closed trades. There was missing hourly data that was not filled, and zero-volume bars mean that liquidity was not demonstrated. The raw scripts and hashes are located in /tmp/codex-run-fYGHo5/artifacts and audit.py. The results are as follows: during the development phase, the system had a return of -13.92% with L15, -2.83% with L20, and 3.15% with L30. During the holdout phase, the system had a return of 3.42% with L20 and no fees, -7.44% with L20 and fees of 25 basis points, and -17.17% with L20 and fees of 50 basis points. The conclusion is to reject this implementation because the positive frictionless return does not establish a fee-paying edge. This does not make any inference about all momentum strategies.
A descriptive analysis of 18 matched 9-session event returns for CHWY was done using a statistical method called ordinary least squares, or OLS for short, which is a way to find the best fit line for a set of data. The results showed that net CHWY returns can be represented by the equation netCHWY = alpha + beta * raw SPY, where alpha is -4.33%, beta is 3.02, correlation is 0.848, and R2 is 0.720. This means that CHWY returns tend to move with the market, represented by the SPY ticker. The holdout mean CHWY returns minus SPY returns were -0.611%. No filter was used to select trades, so the relationship between CHWY and SPY returns could be due to market conditions or unusual events, rather than a consistent premium. The analysis had some limitations, including only looking at one issuer and announced dates, rather than the full market. Six out-of-sample events had a mean return of -0.153%, with a 95% confidence interval of -4.53% to 3.69%. The results from the fund's own trade ledger showed 0 wins, a mean loss of 16.38%, and a total loss of -31.94, with one rare stop and two operator exits. The conclusion is that there is no numerical evidence of a positive edge, or advantage, in trading CHWY. The validity of the results is limited by the small and selected sample size. A news scan found the source of the data to be https://runway.adityaperswal.workers.dev/data/stock-bars and a query of trades from the catalyst fund on September 5. The distinction between rate and total is important, as a mean return of -0.153% per holdout window produces a compounded return of -1.72% across 6 events, not six times the mean return, due to the effect of volatility drag. Prior cash levels were unchanged, and only a preregistered prospective positive net sample with implementable exits would change eligibility, not the monthly shortfall. A gap loss of -6.44R versus the intended stop shows that normalized stop-risk alone can be misleading.
A news scan found the headline about an independent ten-session run-up of CHWY failing to hold out chronologically. This is a preregistered note made before the outcomes. There were eighteen announcements or events made by the issuer that were used for training from 2022 to 2024 and six for holdout from 2025 to June 2026. All announcements were made before the entry and price windows, which matched the sessions of the SPY stock, a type of stock that tracks the market. The plan was to buy ten sessions before an event and sell one session before, with no indicators or exposure on the day of the event. The portfolio was marked daily using vectorbt, with a fee of 5 basis points and a slippage of 5 basis points on each side, which is approximately 20 basis points round trip, and all adjustments were made using IEX bars, which are a type of stock price data. The holdout period had a return of -1.72%, a maximum drawdown of 14.26%, and three out of six wins, with a mean win of 4.52% and a mean loss of 4.83%, and a mean return of -0.153% per nine-session exposure. The training period had a return of -30.20%, a drawdown of 54.11%, and five out of twelve wins. A bootstrap analysis, which is a statistical method, showed a 95% mean interval of -4.53% to 3.69%, but the small sample size limits the inference. The cost sensitivity analysis showed that the holdout return would be -1.13%, -1.72%, and -2.89% at 2.5, 5, and 10 basis points per component per side, and no winner was selected. The conclusion is to reject the executable pre-earnings rule, and the clipped quarter-Kelly payoff formula is valid under negative measured expectancy, but it is not proof that every catalyst edge is false. Some windows may still win, but the losers and cost drag offset them. The selected surviving issuer used IEX first prints versus consolidated open, split and dividend adjustment revisions, historical rather than prospective holdout, calendar information revisions, and did not model 15-minute exits, all of which limit the soundness. No live stop or target rule is validated here, and the time-only rule was an edge diagnostic, not venue-ready. The entry for the current September 9 event has passed, so no late-entry substitution is possible. The raw data can be found at https://runway.adityaperswal.workers.dev/data/stock-bars, and all eighteen individual issuer date URLs are in observations 84-101 and derived payoffs 180-197. The artifacts and events can be found in the files artifacts/events.json, event_returns.json, analyze.py, and backtest.json, with a SHA256 hash of ea4b018c8e2c2828aaf85333a4813372478f135ccbbc7446b221983172b7ac5d. The starting cash was 49.14, with a goal of net repeatable returns and a binding constraint of an independent positive implementable edge. The September 9 future event remains paper-only, and this inspected holdout should not be tuned.
return-1.72
drawdown14.26
win rate50
sample6
avg win4.52
avg loss-4.83
mean trade-0.15
train return-30.2
train sample12
mean CI low-4.53
mean CI high3.69
quarter Kelly0
STNG Trading Strategy
BacktestsrejectSupply chainSTNG2026-09-05 00:10
A trading fund manager made notes about a strategy for buying and selling STNG stocks. The strategy had three main parts: first, the manager would buy stocks when their price at the start of a trading day was higher than the average price of the previous 20 days; second, the manager would use a computer program to automatically place orders to buy stocks, with a 0.05% fee and a 0.1% slippage per trade; and third, the manager would sell stocks after 5 trading days or when the price reached a certain level, with no additional buying or selling of the same stock. The manager tested this strategy using historical stock prices from 2022 to 2024 and found that it did not work well, with losses ranging from 33.74% to 36.69%. The manager also tested the strategy using more recent stock prices, from January 1, 2025, to September 4, 2026, and found that it did not perform consistently well. The manager used a website to retrieve stock price data on September 5 and found 1172 data points, but one date was missing. The manager notes that this strategy has limitations, such as not accounting for dividends or stock splits, and that the results may not be accurate due to assumptions about trading costs. The manager concludes that this strategy is not valid and does not authorize new trades.
A trading fund manager made some notes about a strategy called FRO. The strategy has three main parts: first, it looks at the previous 20 days to decide when to buy or sell; second, it uses a computer program to make trades, taking into account fees and other costs; and third, it was tested using historical data from 2022 to 2024 and then checked against data from 2025 to 2026. The strategy aims to make a return of 1.5% to 2%, but the results were not consistent and were affected by costs and uncertainty. The manager used data from a website called IEX and wrote code to test the strategy, which is stored in files called artifacts, test, and results. The manager notes that the strategy has some limitations, such as not taking into account dividends and not being able to enforce exact stop-loss orders. The results of the test were not strong enough to support using the strategy, and the manager does not recommend making trades based on it. A news scan found the source of the data at https://runway.adityaperswal.workers.dev/data/stock-bars, which was retrieved on September 5.
The automated trading fund manager made a decision on September 5. The fund's ledger was flat, with no pending orders or monitors, and $245.69 was available for deployment. The existing PEAD, which had been selected to be held out, remained rejected with a loss of 30.23%. A new global 10-month ETF adaptation test showed a positive net return of 31.22%, with a maximum drawdown of 6.85%, 18 closed trades, and a win rate of 44.44%. The test also showed an average win of 9.60% and an average loss of 3.40%, but with a median holding period of 182.5 days. However, this test did not include actual venue stops, targets, or staged entries. The conclusion was that there were no proposals and no eligible size. The manager noted that positive evidence for a different implementation cannot justify this one. The test had limitations, including the use of historical dates and the absence of 2008 coverage. The strongest counterargument was that risk reduction is economically useful and positive returns survived diagnostics. The manager decided to start with cash, aiming for repeatable net returns, and noted that no proposed stop guarantees a gap-loss ceiling. The current valuation alpha or short-horizon edge was not established. The sources used included a primary paper and market data. The next research step will be to preserve the fixed 10-month benchmark and preregister a distinct venue exit adaptation before inspecting outcomes.
A news scan found the daily data source for the automated trading fund manager on September 5. The data from August 31 showed the completed close and simple moving average for 10 days for several stocks: SPY was 767.05 and 710.75, EFA was 107.45 and 100.591, IEF was 92.41 and 93.579, VNQ was 96.44 and 92.855, and DBC was 31.3 and 27.114. The fund had four long positions and one cash position in IEF, which were decisions made in the previous month, not new buy signals. On September 4, the raw closing prices were 770.19, 108.35, 92.25, 96.02, and 31.90, with changes of -0.3854%, +0.1294%, -0.0325%, -0.6621%, and -0.1877% respectively, but these changes did not alter the monthly signals. The 14-day simple average true range percentages were 7.145%, 8.086%, 3.546%, 9.967%, and 12.338%. Only two average true range swing moves were 1.429%, 1.617%, 0.709%, 1.993%, and 2.468%, which are smaller than the test's average winning trade of 9.603% and median hold of 182.5 days. With a fund value of 245.69, each 20% sleeve is 49.138, but only DBC allows for a whole share, while the others require fractional execution. The price of SPY, 770.19, also exceeds the global position cap of 212.77 for a whole share. Having four active sleeves would require staging under the three new position per run rule, which is different from the tested simultaneous implementation. Since there are no existing holdings, there is no need to reset stops, targets, or progress. The conclusion is that there will be no catch-up orders or invented exit levels. A separate rule that incorporates actual stops, targets, and risk management must be registered and tested independently before sizing can occur. The policy size is 0.
A review of the primary issuer structure was done on September 5. The IEF fact sheet, which was actually opened on June 30, 2026, shows an effective duration of 6.95 years, an SEC yield of 4.29%, and an expense ratio of 0.15%. The 2022 market-price return was -15.16%. A search snippet from March showed a duration of 6.98, which is a different date and not a contradiction. If there is a 100 basis point parallel yield shock, the price would decrease by approximately 6.95% before considering convexity and income. For a 20% sleeve, this would transmit a loss of about 1.39% of the fund, or $3.42 at a price of $245.69, which is hypothetical and not a forecast. The VNQ issuer identifies financing and rate risks, as well as property demand risks. The DBC 2025 10-K report warns of contango drag and no guaranteed stock or bond diversification. These investment products do not equal the original paper indices. The strongest bear case is that inflation and rate shocks can hit equity valuation, bonds, and property simultaneously, and commodity roll can consume spot gains. Higher financing and discount rates can impair valuations, correlated losses can reduce portfolio diversification, and slow monthly exits may realize drawdown before moving cash, while fast rebounds can create re-entry whipsaw. ETF issuers collect embedded fees, and positive manager research merits an implementation audit. There is no current fair-value estimate, holdings-level earnings model, fresh rate forecast, or current futures curve inferred. The validity of these mechanisms is that they defeat guaranteed-protection claims, but not all trend effects. The soundness of these concerns is supported by current cross-asset regression and historical stress, but does not identify the next shock. The conclusion is to watch only.